COMPARISON OF IRON STATUS AND INSULIN RESISTANCE BETWEEN NON-DIABETIC OFFSPRINGS OF TYPE 2 DIABETICS AND NON-DIABETICS.
Bibliographic record
Abstract
BACKGROUND: Insulin resistance is positively correlated with body iron. It is unclear whether iron is a cause or an outcome of insulin resistance. Insulin resistance precedes type 2 diabetes mellitus. Offspring of type 2 diabetics are insulin resistant as compared to those of the non-diabetics. The present study was designed to compare and correlate insulin resistance with iron parameters (including serum ferritin, transferrin saturation and blood haemoglobin) in non-diabetic offspring of type 2 diabetics and non-diabetic offspring of non-diabetics. METHODS: It was a cross-sectional study, conducted on one hundred and twenty male subjects 20-40 years of age. They were divided into two groups, each group having 60 subjects. Group A included non-diabetic offspring of type 2 diabetics, while Group B included non-diabetic offspring of non-diabetics. Fasting blood sample was taken and examined for glucose, haemoglobin, insulin, iron, TIBC and ferritin. Data was analysed by SPSS-17. RESULTS: Insulin resistance and iron parameters were significantly higher (p<0.05) in non-diabetic offspring of type 2 diabetics as compared to those of the non-diabetics. There was significant positive correlation (p=0.027) between insulin resistance and serum iron in non-diabetic offspring of type 2 diabetics. There was also significant positive correlation between insulin resistance and serum iron, transferrin saturation and haemoglobin in non-diabetic offspring of non-diabetics. CONCLUSION: Non-diabetic offspring of type 2 diabetics have iron load and insulin resistance, that predispose them to the development of type 2 diabetes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".